{"id":"W2186775902","doi":"10.1007/978-3-031-23161-2_483","title":"American Sign Language Detection","year":2024,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Computer Graphics and Games","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Sign (mathematics); American Sign Language; Computer science; Sign language; Linguistics; Artificial intelligence; Mathematics; Philosophy","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005426655,0.0009428863,0.0006567852,0.002556487,0.0007921333,0.00125448,0.0009587178,0.0007145848,0.0370633],"category_scores_gemma":[0.0008997212,0.0003625684,0.0003612913,0.001184551,0.0003094678,0.0008937104,0.001271876,0.0007350579,0.04042866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004358482,"about_ca_system_score_gemma":0.001178981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003817791,"about_ca_topic_score_gemma":0.01053179,"domain_scores_codex":[0.9994753,0.00003481842,0.00002147452,0.0000965107,0.0003096949,0.00006230349],"domain_scores_gemma":[0.9995502,0.00002832069,0.00001389985,0.00006911041,0.0002983465,0.00004012958],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00009663212,0.00004703024,0.0007548126,0.00006227571,0.00001010788,0.00005628971,0.00002448059,0.0005532276,0.02747911,0.003658167,0.1141511,0.8531067],"study_design_scores_gemma":[0.00003000493,0.0001873846,0.0094805,0.0001471572,0.00006326894,0.001727847,0.0001365159,0.06593401,0.1100899,0.008295024,0.8038105,0.00009781218],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02795655,0.003643927,0.5034462,0.001336865,0.001999775,0.0003482731,0.004418574,0.02576907,0.4310808],"genre_scores_gemma":[0.1333713,0.002598451,0.2587711,0.0007873899,0.0002589408,0.0002866323,0.01289263,0.00140003,0.5896334],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0370633,"threshold_uncertainty_score":0.1239891,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006913625300028842,"score_gpt":0.2181898179794743,"score_spread":0.2112761926794455,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}